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Average Estimation of Semiparametric Models for High-Dimensional Longitudinal Data

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Abstract

Model average receives much attention in recent years. This paper considers the semiparametric model averaging for high-dimensional longitudinal data. To minimize the prediction error, the authors estimate the model weights using a leave-subject-out cross-validation procedure. Asymptotic optimality of the proposed method is proved in the sense that leave-subject-out cross-validation achieves the lowest possible prediction loss asymptotically. Simulation studies show that the performance of the proposed model average method is much better than that of some commonly used model selection and averaging methods.

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Authors and Affiliations

  1. School of Mathematical Sciences, Capital Normal University, Beijing, 100048, China

    Zhihao Zhao & Guohua Zou

Authors
  1. Zhihao Zhao
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  2. Guohua Zou
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Corresponding author

Correspondence to Guohua Zou.

Additional information

This research was supported by the Ministry of Science and Technology of China under Grant No. 2016YFB0502301, Academy for Multidisciplinary Studies of Capital Normal University, and the National Natural Science Foundation of China under Grant Nos. 11971323 and 11529101.

This paper was recommended for publication by Editor LI Qizhai.

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Zhao, Z., Zou, G. Average Estimation of Semiparametric Models for High-Dimensional Longitudinal Data. J Syst Sci Complex 33, 2013–2047 (2020). https://doi.org/10.1007/s11424-020-9343-1

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  • DOI: https://doi.org/10.1007/s11424-020-9343-1

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